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Congestion traffic flow traceability analysis method

An analysis method and technology of traffic flow, applied in the field of traceability analysis of congested traffic flow, which can solve the problems of lack of congestion relief and no consideration of traffic flow source information in congested areas, and achieve the effect of improving inference accuracy and good adaptability

Active Publication Date: 2020-03-17
TONGJI UNIV
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AI Technical Summary

Problems solved by technology

However, the above-mentioned congestion mitigation measures do not consider the source information of traffic flow in congested areas, so they do not have the ability to alleviate congestion at the network level
[0007] Existing problems: The existing traffic flow traceability methods do not consider the source information of traffic flow in congested areas, so they do not have the ability to alleviate congestion from the network level

Method used

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  • Congestion traffic flow traceability analysis method
  • Congestion traffic flow traceability analysis method
  • Congestion traffic flow traceability analysis method

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Embodiment

[0046] This embodiment provides a method for analyzing the source of congestion traffic flow, such as figure 1 As shown, there are two steps:

[0047] Step S1: Based on the automatic vehicle identifier data of the vehicles in the congested area and the source data of the vehicle road, construct a deep neural network multi-classification model to obtain the spatial source of the vehicle;

[0048] Step S2: Based on the spatial source of the vehicle and the data of the automatic vehicle identifier, construct a deep neural network regression model to obtain the time traceability result of the vehicle.

[0049] in particular:

[0050] 1. Step S1 includes:

[0051] Step S11: performing one-hot encoding on the data of the automatic vehicle identifier and the data of the source of the vehicle road to obtain the one-hot encoded data of the automatic vehicle identifier and the one-hot encoded data of the source of the vehicle road;

[0052] Step S12: constructing a deep neural networ...

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Abstract

The invention relates to a congestion traffic flow traceability analysis method, which comprises the following steps of S1, constructing a deep neural network multi-classification model based on automatic vehicle identifier data and vehicle road source data of vehicles in a congestion area, and obtaining space sources of the vehicles; and S2, constructing a deep neural network regression model based on the space source of the vehicle and the data of the automatic vehicle identifier to obtain a time tracing result of the vehicle. Compared with the prior art, the method has the advantages that the source information of the traffic flow in the congestion area is considered, so that the capacity of relieving congestion from the network level is achieved, and a new research view angle for relieving congestion is provided; compared with a traditional machine learning algorithm, the reasoning accuracy can be obviously improved.

Description

technical field [0001] The invention relates to the field of traffic control, in particular to a method for tracing the source of traffic jams. Background technique [0002] Tracing the source of congestion traffic flow refers to tracing the source of traffic flow at the time and space levels. Among them, spatial traceability refers to tracing the starting position of the vehicle outside a certain spatial range, and time tracing refers to estimating the travel time required for the vehicle to reach a specific spatial location from the starting point. Since the penetration rate of Internet-connected vehicles will continue to maintain a low market penetration rate for a long period of time in the future, it is impossible to accurately judge the source of vehicles in congested areas. The traceability analysis of congested traffic flow traces traffic by analyzing existing incomplete data. The source of traffic is expected to be a key input information for network traffic contro...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24Y02T10/40
Inventor 马万经袁见俞春辉王玲
Owner TONGJI UNIV
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